By Pooja Toshniwal Paharia Combining large language models with traditional methods enhances accuracy in identifying early ...

The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual ...

Safran Stock Market Prediction est une application de bureau développée avec Electron.js qui utilise deux modèles d'intelligence artificielle, LSTM et MLP Dense, pour prédire les prix des actions de ...

The results underscore the effectiveness of boosting algorithms such as CatBoost, LightGBM, and XGBoost in managing time series data. The study’s heatmap visualization provided a comprehensive ...

This project enhances agricultural weather forecasting by predicting solar radiation (SRAD) using machine learning and deep learning models, including KNN, Random Forest, XGBoost, LSTM, and hybrid ...

evaluated various machine-learning approaches to achieve this, including: (1) feature-engineered approaches, including logistic regression, XGBoost, and shallow artificial neural networks ... Fast ...

Vikki Velasquez is a researcher and writer who has managed, coordinated, and directed various community and nonprofit organizations. She has conducted in-depth research on social and economic ...

Thus, we propose end-to-end models combining Functional Brain Network (FBN) and Siamese Long Short-Term Memory model (Siam-LSTM) and apply them to a Virtual Reality Motion Sickness (VRMS) recognition ...